In this study, a novel metaheuristic optimization algorithm, gradient-based optimizer (GBO) is proposed. The GBO, inspired by the gradient-based Newton's method, uses two main operators: gradient search rule (GSR) and local escaping operator (LEO) and a set of vectors to explore the search ...
并使用闲置LocalSyncParallelOptimizer载入数据(所以要有多个LocalSyncParallelOptimizer才好),然后将其放入ready_optimizers中,这也就是replay buffer的输入管道,learner从replay buffer中拿optimizer,然后运行optimize计算图,根据情况踢出optimizer,将结果放入inqueue中,所以不管是多learner还是单learner的情况,对外部的AsyncSampl...
This paper considers setting different dips for different sub-faults to fit the actual rupture situation based on the fault rupture of the 2013 Lushan<italic>M </italic> <inf loc="post">S </inf>7.0 earthquake. Meanwhile, c
Adaptive particle swarm optimizerGradientImproving extremal optimizationMutation strategyMost real-world applications can be formulated as optimization problems, which commonly suffer from being trapped into the local optima. In this paper, we make full use of the global search capability of particle swarm...
In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in a radial distribution network considering uncertainties of renewable generation and network demand.
Firstly, we use the gradient based optimizer (GBO) in nonlinear inversion to obtain the source parameters of this seismic fault. The inversion results indicate that the strike of the fault is 206.52°, the dip is 44.10°, the length is 21.92 km, and the depth is 12.79 km. To refine the...
README GPL-3.0 license GTOP:Gradient-Based Trajectory Optimizer (This repo is mainly developed and maintained byBoyu Zhou, please contace him if necessary) 1.Introduction Gradient-Based Online Safe Trajectory Generation is trajectory optimization framework, for generating a safe, smooth and dynamically...
The optimizer, or better the numerical optimization algorithm, drives the optimization iterations. A parameter vector \(\textbf{x}\) describes the current state and is passed to the optimization processor. The optimization processor is the main interface for a numerical optimization algorithm and takes...
In addition to a highly-flexible optimization engine for general-purpose gradient-based optimization, Boulder Opal also features a gradient-free optimizer which can be directly applied to model-based control optimization for arbitrary-dimensional quantum systems. The gradient-free optimizer...
“Overcoming catastrophic forgetting in neural networks” by Kirkpatrick, P. et al., PNAS (2017). In one or more implementations, the fine-tuning module208optimizes the weights θ using a gradient-based optimizer, such as a number (e.g., 50) of optimization steps using an Adam optimizer....